关键词: 模式识别, 遥感影像, 目标检测, 注意力机制, 多尺度, 优化算法

Abstract:

The problem of target detection in remote sensing images has always been a hot and difficult point in the field of remote sensing image processing. Traditional detection algorithms have low performance when solving complex scenes with large scale differences. However, it is difficult to balance the accuracy and real-time performance of remote sensing targets using deep learning. In response to this problem, this paper designs a lightweight network that uses multi-scale fusion feature detection targets, and proposes an attention mechanism that can generate pixel adaptive feature weights from three dimensions to help extract salient features. The latest optimization algorithm is used to improve the performance of the model, while reducing the amount of calculations and ensuring the detection accuracy. Experimental results show that the model MAP@0.5 can reach 0.945 and F1 can reach 0.841, and the detection speed can meet the real-time requirements.

Key words: pattern recognition, remote sensing image, target detection, attention mechanism, multi-scale, optimization algorithm